{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 769,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np  \n",
    "import pandas as pd \n",
    "\n",
    "from sklearn.metrics import r2_score  \n",
    "\n",
    "import matplotlib.pyplot as plt   \n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 770,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed   cnt  \n",
       "0           2  0.344167  0.363625  0.805833   0.160446   985  \n",
       "1           2  0.363478  0.353739  0.696087   0.248539   801  \n",
       "2           1  0.196364  0.189405  0.437273   0.248309  1349  \n",
       "3           1  0.200000  0.212122  0.590435   0.160296  1562  \n",
       "4           1  0.226957  0.229270  0.436957   0.186900  1600  "
      ]
     },
     "execution_count": 770,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dpath = './Bike-Sharing-Dataset/'\n",
    "data0 = pd.read_csv(dpath +\"day.csv\")#data0原始数据，下一步将删除casual，registered\n",
    "data = data0.drop(['casual','registered'],axis=1)\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 771,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(731, 14)"
      ]
     },
     "execution_count": 771,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 772,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 14 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(9), object(1)\n",
      "memory usage: 80.0+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 773,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#将2011数据和2012数据拆分成 data_train 和 data_test。\n",
    "data['dteday'] = pd.to_datetime(data['dteday'])\n",
    "data_train = data[(data['dteday'] >=pd.to_datetime('20110101')) & (data['dteday'] <= pd.to_datetime('20111231'))]\n",
    "data_test = data[(data['dteday'] >=pd.to_datetime('20120101')) & (data['dteday'] <= pd.to_datetime('20121231'))]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 774,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2011-01-03</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.437273</td>\n",
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       "      <td>1349</td>\n",
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       "      <td>2011-01-04</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant     dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1 2011-01-01       1   0     1        0        6           0   \n",
       "1        2 2011-01-02       1   0     1        0        0           0   \n",
       "2        3 2011-01-03       1   0     1        0        1           1   \n",
       "3        4 2011-01-04       1   0     1        0        2           1   \n",
       "4        5 2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed   cnt  \n",
       "0           2  0.344167  0.363625  0.805833   0.160446   985  \n",
       "1           2  0.363478  0.353739  0.696087   0.248539   801  \n",
       "2           1  0.196364  0.189405  0.437273   0.248309  1349  \n",
       "3           1  0.200000  0.212122  0.590435   0.160296  1562  \n",
       "4           1  0.226957  0.229270  0.436957   0.186900  1600  "
      ]
     },
     "execution_count": 774,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 775,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
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       "      <th>yr</th>\n",
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       "      <td>2011-12-27</td>\n",
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       "      <td>2011-12-28</td>\n",
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       "      <td>0.503913</td>\n",
       "      <td>0.293961</td>\n",
       "      <td>2302</td>\n",
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       "      <th>362</th>\n",
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       "      <td>2011-12-29</td>\n",
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       "      <th>364</th>\n",
       "      <td>365</td>\n",
       "      <td>2011-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>0.414121</td>\n",
       "      <td>0.615833</td>\n",
       "      <td>0.220154</td>\n",
       "      <td>2485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     instant     dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "360      361 2011-12-27       1   0    12        0        2           1   \n",
       "361      362 2011-12-28       1   0    12        0        3           1   \n",
       "362      363 2011-12-29       1   0    12        0        4           1   \n",
       "363      364 2011-12-30       1   0    12        0        5           1   \n",
       "364      365 2011-12-31       1   0    12        0        6           0   \n",
       "\n",
       "     weathersit      temp     atemp       hum  windspeed   cnt  \n",
       "360           2  0.325000  0.327633  0.762500   0.188450  1162  \n",
       "361           1  0.299130  0.279974  0.503913   0.293961  2302  \n",
       "362           1  0.248333  0.263892  0.574167   0.119412  2423  \n",
       "363           1  0.311667  0.318812  0.636667   0.134337  2999  \n",
       "364           1  0.410000  0.414121  0.615833   0.220154  2485  "
      ]
     },
     "execution_count": 775,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.tail()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 776,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365, 14)"
      ]
     },
     "execution_count": 776,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 777,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>366</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.375621</td>\n",
       "      <td>0.692500</td>\n",
       "      <td>0.192167</td>\n",
       "      <td>2294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>367</td>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.273043</td>\n",
       "      <td>0.252304</td>\n",
       "      <td>0.381304</td>\n",
       "      <td>0.329665</td>\n",
       "      <td>1951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>368</td>\n",
       "      <td>2012-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.126275</td>\n",
       "      <td>0.441250</td>\n",
       "      <td>0.365671</td>\n",
       "      <td>2236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>369</td>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.107500</td>\n",
       "      <td>0.119337</td>\n",
       "      <td>0.414583</td>\n",
       "      <td>0.184700</td>\n",
       "      <td>2368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>370</td>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.265833</td>\n",
       "      <td>0.278412</td>\n",
       "      <td>0.524167</td>\n",
       "      <td>0.129987</td>\n",
       "      <td>3272</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     instant     dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "365      366 2012-01-01       1   1     1        0        0           0   \n",
       "366      367 2012-01-02       1   1     1        1        1           0   \n",
       "367      368 2012-01-03       1   1     1        0        2           1   \n",
       "368      369 2012-01-04       1   1     1        0        3           1   \n",
       "369      370 2012-01-05       1   1     1        0        4           1   \n",
       "\n",
       "     weathersit      temp     atemp       hum  windspeed   cnt  \n",
       "365           1  0.370000  0.375621  0.692500   0.192167  2294  \n",
       "366           1  0.273043  0.252304  0.381304   0.329665  1951  \n",
       "367           1  0.150000  0.126275  0.441250   0.365671  2236  \n",
       "368           2  0.107500  0.119337  0.414583   0.184700  2368  \n",
       "369           1  0.265833  0.278412  0.524167   0.129987  3272  "
      ]
     },
     "execution_count": 777,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 778,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>727</td>\n",
       "      <td>2012-12-27</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.254167</td>\n",
       "      <td>0.226642</td>\n",
       "      <td>0.652917</td>\n",
       "      <td>0.350133</td>\n",
       "      <td>2114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>728</td>\n",
       "      <td>2012-12-28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.253333</td>\n",
       "      <td>0.255046</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>0.155471</td>\n",
       "      <td>3095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>729</td>\n",
       "      <td>2012-12-29</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.253333</td>\n",
       "      <td>0.242400</td>\n",
       "      <td>0.752917</td>\n",
       "      <td>0.124383</td>\n",
       "      <td>1341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>730</td>\n",
       "      <td>2012-12-30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.255833</td>\n",
       "      <td>0.231700</td>\n",
       "      <td>0.483333</td>\n",
       "      <td>0.350754</td>\n",
       "      <td>1796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>731</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.215833</td>\n",
       "      <td>0.223487</td>\n",
       "      <td>0.577500</td>\n",
       "      <td>0.154846</td>\n",
       "      <td>2729</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     instant     dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "726      727 2012-12-27       1   1    12        0        4           1   \n",
       "727      728 2012-12-28       1   1    12        0        5           1   \n",
       "728      729 2012-12-29       1   1    12        0        6           0   \n",
       "729      730 2012-12-30       1   1    12        0        0           0   \n",
       "730      731 2012-12-31       1   1    12        0        1           1   \n",
       "\n",
       "     weathersit      temp     atemp       hum  windspeed   cnt  \n",
       "726           2  0.254167  0.226642  0.652917   0.350133  2114  \n",
       "727           2  0.253333  0.255046  0.590000   0.155471  3095  \n",
       "728           2  0.253333  0.242400  0.752917   0.124383  1341  \n",
       "729           1  0.255833  0.231700  0.483333   0.350754  1796  \n",
       "730           2  0.215833  0.223487  0.577500   0.154846  2729  "
      ]
     },
     "execution_count": 778,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.tail()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 779,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(366, 14)"
      ]
     },
     "execution_count": 779,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 780,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.0</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>183.000000</td>\n",
       "      <td>2.498630</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.526027</td>\n",
       "      <td>0.027397</td>\n",
       "      <td>3.008219</td>\n",
       "      <td>0.684932</td>\n",
       "      <td>1.421918</td>\n",
       "      <td>0.486665</td>\n",
       "      <td>0.466835</td>\n",
       "      <td>0.643665</td>\n",
       "      <td>0.191403</td>\n",
       "      <td>3405.761644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>105.510663</td>\n",
       "      <td>1.110946</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.452584</td>\n",
       "      <td>0.163462</td>\n",
       "      <td>2.006155</td>\n",
       "      <td>0.465181</td>\n",
       "      <td>0.571831</td>\n",
       "      <td>0.189596</td>\n",
       "      <td>0.168836</td>\n",
       "      <td>0.148744</td>\n",
       "      <td>0.076890</td>\n",
       "      <td>1378.753666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>431.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>92.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>0.321954</td>\n",
       "      <td>0.538333</td>\n",
       "      <td>0.135583</td>\n",
       "      <td>2132.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>183.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.479167</td>\n",
       "      <td>0.472846</td>\n",
       "      <td>0.647500</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>3740.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>274.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.656667</td>\n",
       "      <td>0.612379</td>\n",
       "      <td>0.742083</td>\n",
       "      <td>0.235075</td>\n",
       "      <td>4586.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>365.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.849167</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>6043.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season     yr        mnth     holiday     weekday  \\\n",
       "count  365.000000  365.000000  365.0  365.000000  365.000000  365.000000   \n",
       "mean   183.000000    2.498630    0.0    6.526027    0.027397    3.008219   \n",
       "std    105.510663    1.110946    0.0    3.452584    0.163462    2.006155   \n",
       "min      1.000000    1.000000    0.0    1.000000    0.000000    0.000000   \n",
       "25%     92.000000    2.000000    0.0    4.000000    0.000000    1.000000   \n",
       "50%    183.000000    3.000000    0.0    7.000000    0.000000    3.000000   \n",
       "75%    274.000000    3.000000    0.0   10.000000    0.000000    5.000000   \n",
       "max    365.000000    4.000000    0.0   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  365.000000  365.000000  365.000000  365.000000  365.000000  365.000000   \n",
       "mean     0.684932    1.421918    0.486665    0.466835    0.643665    0.191403   \n",
       "std      0.465181    0.571831    0.189596    0.168836    0.148744    0.076890   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.325000    0.321954    0.538333    0.135583   \n",
       "50%      1.000000    1.000000    0.479167    0.472846    0.647500    0.186900   \n",
       "75%      1.000000    2.000000    0.656667    0.612379    0.742083    0.235075   \n",
       "max      1.000000    3.000000    0.849167    0.840896    0.972500    0.507463   \n",
       "\n",
       "               cnt  \n",
       "count   365.000000  \n",
       "mean   3405.761644  \n",
       "std    1378.753666  \n",
       "min     431.000000  \n",
       "25%    2132.000000  \n",
       "50%    3740.000000  \n",
       "75%    4586.000000  \n",
       "max    6043.000000  "
      ]
     },
     "execution_count": 780,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 781,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "cols = data_train.columns\n",
    "data_train_corr = data_train.corr().abs()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 782,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PV6KjYjKX8XQjMjIanU6Hk6Njpvzm/Djza86du8APP/yao5kjI6Lx9HRPl9mNqOirmcqU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\nwy5tiY+7Q90azZjz2yKGjx4AgF6vZ8acH/m2/xic67SkrUdXEhIeA9B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6c+xIKFs272LZ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\n3c+aMnTnLwza+hO9Vgwnm1POuLJmgzsyZMdkhu6aQqtR3SyY2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p2OJUvX4Ovrx+hR/Tl5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xfb8c978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(14,9))\n",
    "sns.heatmap(data_train_corr,annot=True)\n",
    "sns.heatmap(data_train_corr,mask=data_train_corr<1,cbar=False)\n",
    "plt.show()\n",
    "#通过热图，减少不相关，或者类似数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 783,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#更具上图进行相应的数据调整\n",
    "y_train = data_train['cnt'].values\n",
    "x_train = data_train.drop(['instant','dteday','mnth','holiday','weekday','workingday','yr','hum','cnt'],axis=1)\n",
    "y_test = data_test['cnt'].values\n",
    "x_test = data_test.drop(['instant','dteday','mnth','holiday','weekday','workingday','yr','hum','cnt'],axis=1)\n",
    "full_x_data = data.drop(['instant','dteday','mnth','holiday','weekday','workingday','yr','hum','cnt'],axis=1)\n",
    "columns = full_x_data.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 784,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.160446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.248539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.248309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.160296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.186900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season  weathersit      temp     atemp  windspeed\n",
       "0       1           2  0.344167  0.363625   0.160446\n",
       "1       1           2  0.363478  0.353739   0.248539\n",
       "2       1           1  0.196364  0.189405   0.248309\n",
       "3       1           1  0.200000  0.212122   0.160296\n",
       "4       1           1  0.226957  0.229270   0.186900"
      ]
     },
     "execution_count": 784,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 785,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.375621</td>\n",
       "      <td>0.192167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.273043</td>\n",
       "      <td>0.252304</td>\n",
       "      <td>0.329665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.126275</td>\n",
       "      <td>0.365671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.107500</td>\n",
       "      <td>0.119337</td>\n",
       "      <td>0.184700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.265833</td>\n",
       "      <td>0.278412</td>\n",
       "      <td>0.129987</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     season  weathersit      temp     atemp  windspeed\n",
       "365       1           1  0.370000  0.375621   0.192167\n",
       "366       1           1  0.273043  0.252304   0.329665\n",
       "367       1           1  0.150000  0.126275   0.365671\n",
       "368       1           2  0.107500  0.119337   0.184700\n",
       "369       1           1  0.265833  0.278412   0.129987"
      ]
     },
     "execution_count": 785,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 786,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "ss_x = MinMaxScaler()\n",
    "ss_y = MinMaxScaler()\n",
    "\n",
    "ss_x = StandardScaler()\n",
    "ss_y = StandardScaler()\n",
    "\n",
    "x_train = ss_x.fit_transform(x_train)\n",
    "x_test = ss_x.transform(x_test)\n",
    "y_train = ss_y_train.fit_transform(y_train.reshape(-1, 1))\n",
    "y_test = ss_y_test.fit_transform(y_test.reshape(-1, 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 787,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[0.684558407551]</td>\n",
       "      <td>temp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[0.291466196546]</td>\n",
       "      <td>season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[-0.0584982718177]</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[-0.108706153069]</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[-0.259995061592]</td>\n",
       "      <td>weathersit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 coef     columns\n",
       "2    [0.684558407551]        temp\n",
       "0    [0.291466196546]      season\n",
       "3  [-0.0584982718177]       atemp\n",
       "4   [-0.108706153069]   windspeed\n",
       "1   [-0.259995061592]  weathersit"
      ]
     },
     "execution_count": 787,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr = LinearRegression()\n",
    "\n",
    "lr.fit(x_train,y_train)\n",
    "\n",
    "y_test_pred_lr = lr.predict(x_test)\n",
    "y_train_pred_lr = lr.predict(x_train)\n",
    "\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef\":list((lr.coef_.T))})\n",
    "fs.sort_values(by=['coef'],ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 788,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LinearRegression on test is 0.647687168497\n",
      "The r2 score of LinearRegression on train is 0.753472575726\n"
     ]
    }
   ],
   "source": [
    "print 'The r2 score of LinearRegression on test is', r2_score(y_test, y_test_pred_lr)\n",
    "print 'The r2 score of LinearRegression on train is', r2_score(y_train, y_train_pred_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 789,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gg/KUpzxl7I05CdXa5nNXDfpKL0ry49baa0dM33d4vl6q6nVJntxae9Hm1rVo0aK27ooZ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\nDgl5AAAdEvIAADok5AEAdOj/A9ayfkhVTMChAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12b8c1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(9, 5)) \n",
    "f.tight_layout() \n",
    "ax.hist(y_train - y_train_pred_lr,bins=50, label='Residuals Linear', color='y', alpha=.5); \n",
    "ax.set_title(\"Histogram of Residuals\") \n",
    "ax.legend(loc='best');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 790,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A17p9pTLTAsjJysTUJZsV71deGUTJA1c0/Nw1d5XicYergtg88wrF3wHqhcizCrbjRHVt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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a4d710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 7))\n",
    "plt.scatter(y_train, y_train_pred_lr)\n",
    "plt.plot([-1.5, 1.5], [-1.5, 1.5], '--k')   \n",
    "plt.axis('tight')\n",
    "plt.xlabel('True price')\n",
    "plt.ylabel('Predicted price')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 791,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.29027233, -0.25895529,  0.48804072,  0.13910667, -0.1050966 ])"
      ]
     },
     "execution_count": 791,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "\n",
    "\n",
    "sgdr = SGDRegressor(max_iter=1000)\n",
    "\n",
    "\n",
    "sgdr.fit(x_train, y_train)\n",
    "\n",
    "sgdr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 792,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of default measurement of SGDRegressor on test is 0.651275566615\n",
      "The value of default measurement of SGDRegressor on train is 0.753218449964\n"
     ]
    }
   ],
   "source": [
    "print 'The value of default measurement of SGDRegressor on test is', sgdr.score(x_test, y_test)\n",
    "print 'The value of default measurement of SGDRegressor on train is', sgdr.score(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 793,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.652585741624\n",
      "The r2 score of RidgeCV on train is 0.752607233027\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.linear_model import  RidgeCV\n",
    "\n",
    "alphas = [ 0.001,0.01, 0.1, 1, 10,100,1000]\n",
    "\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "\n",
    "ridge.fit(x_train, y_train)    \n",
    "\n",
    "y_test_pred_ridge = ridge.predict(x_test)\n",
    "y_train_pred_ridge = ridge.predict(x_train)\n",
    "\n",
    "print 'The r2 score of RidgeCV on test is', r2_score(y_test, y_test_pred_ridge)\n",
    "print 'The r2 score of RidgeCV on train is', r2_score(y_train, y_train_pred_ridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 794,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GfTUivpiMvwP4PKfmR3ZExHVp1Wdmdr48tiNPxPT8yuyYND9ZrANyEbEzIoaBh4E7SwdE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\nmZlV5LAwM7OK/j9RxP8xe6LJhQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xfcda898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('alpha is:', 10.0)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[0.684558407551]</td>\n",
       "      <td>[0.349152267637]</td>\n",
       "      <td>temp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[0.291466196546]</td>\n",
       "      <td>[0.28335208251]</td>\n",
       "      <td>season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[-0.0584982718177]</td>\n",
       "      <td>[0.273064003459]</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[-0.108706153069]</td>\n",
       "      <td>[-0.10178843145]</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[-0.259995061592]</td>\n",
       "      <td>[-0.252012844182]</td>\n",
       "      <td>weathersit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              coef_lr         coef_ridge     columns\n",
       "2    [0.684558407551]   [0.349152267637]        temp\n",
       "0    [0.291466196546]    [0.28335208251]      season\n",
       "3  [-0.0584982718177]   [0.273064003459]       atemp\n",
       "4   [-0.108706153069]   [-0.10178843145]   windspeed\n",
       "1   [-0.259995061592]  [-0.252012844182]  weathersit"
      ]
     },
     "execution_count": 794,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "\n",
    "#这是为了标出最佳参数的位置，不是必须\n",
    "#plt.plot(np.log10(ridge.alpha_)*np.ones(3), [0.28, 0.29, 0.30])\n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print ('alpha is:', ridge.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 795,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is 0.648958229264\n",
      "The r2 score of LassoCV on train is 0.753429580516\n"
     ]
    }
   ],
   "source": [
    "#### Lasso／L1正则\n",
    "# class sklearn.linear_model.LassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, \n",
    "#                                    normalize=False, precompute=’auto’, max_iter=1000, \n",
    "#                                    tol=0.0001, copy_X=True, cv=None, verbose=False, n_jobs=1,\n",
    "#                                    positive=False, random_state=None, selection=’cyclic’)\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "#设置超参数搜索范围\n",
    "#alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "\n",
    "#生成一个LassoCV实例\n",
    "#lasso = LassoCV(alphas=alphas)  \n",
    "lasso = LassoCV()  \n",
    "\n",
    "#训练（内含CV）\n",
    "lasso.fit(x_train, y_train)  \n",
    "\n",
    "#测试\n",
    "y_test_pred_lasso = lasso.predict(x_test)\n",
    "y_train_pred_lasso = lasso.predict(x_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print 'The r2 score of LassoCV on test is', r2_score(y_test, y_test_pred_lasso)\n",
    "print 'The r2 score of LassoCV on train is', r2_score(y_train, y_train_pred_lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 796,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9Vm1rPG1rQBlpKRjRN9hBSyhMczBMU2souszhY36N7PRUstMDDMpIJScjQE5G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\ngkFERNr5/7mrYXQy2plOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf9b8358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('alpha is:', 0.0027222048663155386)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lasso</th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.624972</td>\n",
       "      <td>[0.684558407551]</td>\n",
       "      <td>[0.349152267637]</td>\n",
       "      <td>temp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.289246</td>\n",
       "      <td>[0.291466196546]</td>\n",
       "      <td>[0.28335208251]</td>\n",
       "      <td>season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[-0.0584982718177]</td>\n",
       "      <td>[0.273064003459]</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.105635</td>\n",
       "      <td>[-0.108706153069]</td>\n",
       "      <td>[-0.10178843145]</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.257297</td>\n",
       "      <td>[-0.259995061592]</td>\n",
       "      <td>[-0.252012844182]</td>\n",
       "      <td>weathersit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   coef_lasso             coef_lr         coef_ridge     columns\n",
       "2    0.624972    [0.684558407551]   [0.349152267637]        temp\n",
       "0    0.289246    [0.291466196546]    [0.28335208251]      season\n",
       "3    0.000000  [-0.0584982718177]   [0.273064003459]       atemp\n",
       "4   -0.105635   [-0.108706153069]   [-0.10178843145]   windspeed\n",
       "1   -0.257297   [-0.259995061592]  [-0.252012844182]  weathersit"
      ]
     },
     "execution_count": 796,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "#plt.plot(np.log10(lasso.alphas_)*np.ones(3), [0.3, 0.4, 1.0])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()    \n",
    "            \n",
    "print ('alpha is:', lasso.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T)), \"coef_lasso\":list((lasso.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 797,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9Vm1rPG1rQBlpKRjRN9hBSyhMczBMU2souszhY36N7PRUstMDDMpIJScjQE5G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\ngkFERNr5/7mrYXQy2plOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf4d93c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('alpha is:', 0.0027222048663155386)\n"
     ]
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "#plt.plot(np.log10(lasso.alphas_)*np.ones(3), [0.3, 0.4, 1.0])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()    \n",
    "            \n",
    "print ('alpha is:', lasso.alpha_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
